There are numerous instances where one or more properties of a material are preferably analyzed at one or more locations removed from an analytical laboratory where testing would normally be conducted. For example, agricultural products may be analyzed for the presence and concentration of certain components during the crop growing stage, at harvesting, during transportation, or after the product has been stored, as at a grain silo. Other non-limiting instances where this type of analysis would be useful include applications in the processed food industry, the mining industry, the chemical industry, the finished hard goods industries, and a variety of service, retail sales, and medical industries.
In the absence of equipment or skilled personnel for conducting sample analysis at the location of the sample, substantial time delays can result in initiating and completing an analysis. Thus in the case of the analysis of an agricultural product such as an oil seed which would be harvested within a narrow window of time, traditionally specific characteristics of the seed are determined by a laboratory. This is due to the fact that the equipment and skilled personnel generally required to conduct such analyses are not normally available to the farmer or even to the silo operator. Thus, if an oil seed is to be analyzed, a sample is taken from the farmer's truck or from the silo operator and sent to an independent laboratory for analysis. It is not uncommon in this situation for the sample to require one day for forwarding to the laboratory, two days for the laboratory to conduct the analysis, and an additional day for the results to be returned to the silo operator. Thus, a particular lot of oil seed may require four days to be analyzed. Where the value of the oil seed is dependent on the analysis, utilizing laboratory analysis results in substantial delays to the farmer in obtaining a value for his crop, to the silo operator in valuing the crop and determining the market into which the seed will be sold, and to the ultimate purchaser of the oil seed. When one considers that in the case of oil seed harvesting the oil seed crop typically is processed within a narrow window of time over a wide geographic area, the individual delays described above become multiplied at each silo within the crop growing region.
Alternatively, it is known to analyze certain components of a particular agricultural product at the location where the material is either grown, harvested, transported, or stored. Nevertheless, this resultant analysis of the product may not be directly comparable to an analysis of the same agricultural product in a different location, even though using the same methodology. Even when the same sample is analyzed at different locations, differences in analytical results may arise, for example, because of a difference in environmental conditions at one analysis location relative to the other or because of a difference in the performance of the analyzers. Results may also differ because of variations in the procedure of presenting the sample to different analyzers.
It may be convenient or necessary for on-site analyzers to be able to be easily transported from one location to another. A portable sensor unit or spectrometer is one that is sufficiently compact and robust to permit it to be transported to alternate testing locations as needed. These units are able to be removed from service and returned to service quickly for transportation to and use at a desired site for analysis. The analytical instruments for such analysis must be rugged and capable of making repetitive analyses without extensive recalibration by a skilled operator and with little or no variation over the course of use of the unit.
Because the analysis of, for example, a particular agricultural product may need to be determined at locations over a wide geographic area within a narrow time frame, it may become impractical to conduct the analyses using only one instrument. Generally it then becomes necessary to test these products at multiple sites with multiple analyzers. Under these circumstances, each of the analyzers must be calibrated so that the output results from the various analyzers can be properly compared. Depending on the type of analysis, with traditional techniques the analytical results of a particular agricultural product using multiple analyzers may vary because of different effects resulting from the environmental, instrument, or sample presentation variations discussed above. To address this, either each analysis should be conducted under the same environmental and sample presentation test conditions, or each analysis should be completed with the ability to compensate for differences in temperature, humidity and other relevant environmental variations in generating data by the individual analyzers. Further, results generated by different analyzers may differ because of inherent manufacturing differences between the highly sensitive instrument components and differences in the precise assembly of the components, the differences becoming more pronounced over time producing instrument drift. As a result, no two analytical instruments are precisely identical to one another, so accommodation must be made in considering the results generated by an analyzer when comparing the results with those from other units. While work has been done to develop practical methods for transferring multivariate calibrations between instruments, for example as discussed in U.S. Pat. No. 5,459,677, these methods require that some instrument, sometimes called a reference instrument or a master instrument, be maintained in some known or reproducible state or be capable of being brought into a reproducible and well defined state to achieve instrument standardization. Then, a master calibration model developed on one instrument can be transferred to a number of target instruments. However, all calibration transfer and instrument standardization methods require additional steps to be taken at various times potentially over a range of time intervals after the initial calibration transfer. For example, the analyst may have to evaluate a set of calibration transfer samples on the target instruments after the initial calibration transfer, usually by a skilled operator, and adjust either the models or the instruments so that the response from the target instruments agrees with the response from the master or reference instrument. Further, the measurement conditions of the material samples being analyzed, for example the sample temperature, may also be different at the various sites. Again, accommodations must be made in considering the results generated from those material samples.
Another matter to be considered in conducting remote analyses of materials such as agricultural products is the amount and quality of information desired from the analysis, and the demands placed on the analyzer. Generally, as the analyzer is able to perform more sophisticated analysis, the analyzer itself becomes more complex, a higher level of training is required to operate the analyzer and interpret the results, and the weight and size of the instrument may increase as a result. An analyzer capable of undertaking more complex analyses is generally more susceptible to damage and to generating inaccurate results by the process of moving the analyzer from site to site, utilizing the analyzer under varying conditions, and the like. Consequently, the results from such an instrument are more likely to change and thus render comparison between various remote analyzers more difficult or even impossible.
The need to be able to generate comparable, statistically equivalent analyses of materials at remote site locations can extend to a wide range of materials in addition to agricultural products such as, but not limited to, manufactured products, natural phenomena, ores, renewable raw materials, fuels, and living tissue.
The combination of a calibration model with an analytical instrument to generate a predicted result has been practiced. It is known to use, for example, calibration models associated with near-infrared, mid-infrared, and Raman spectrometers in commercial processes to monitor the status of chemical reactions. This monitoring capability can involve the generation of results from an analytical method with the application of statistical analysis and calibration models to interpret and quantify the data. For example, in the manufacture of carboxylic acids and derivatives from fats and oils, it is known to use near-infrared spectrometers loaded with the appropriate chemometric software to measure a number of properties of the carboxylic acids and their derivatives. This monitoring can be done during the manufacturing process on intermediate product, as well as on the finished product. The spectrometer can be operated in a stand-alone mode with the operator bringing samples to the spectrometer for at-line analysis. Alternatively, the spectrometer can be connected in-line to enable monitoring of the process stream as the manufacturing operation proceeds. Thus, two commercially available near-infrared spectrometers such as the Bomem MB-160 FT-NIR spectrometer loaded with HOVAL software (such as Version 1.6, 1992) and AIRS software (such as Version 1.54, 1999) from Bomem Inc., Canada, and the Bruker Vector 22/N spectrometer loaded with OPUS-NT Quant-2 software (such as Version 2.6, 1999) from Bruker Optik GmbH, Germany have been used to analyze intermediate and finished carboxylic acid products for acid value, iodine value, titer, viscosity, hydroxyl value, saponification value, composition of fatty materials and derivatives, and for the presence of carboxylic acid methyl ester contaminants in a specific carboxylic acid.
The calibration models for evaluating the above properties were derived from the Grams-PLS plus (Version 3.01 B, 1994, Galactic Industries Corporation) and Bruker OPUS Quant-2 software. In those instances where more than one data acquisition device was used to generate predicted results for a particular property of interest, individual calibration models were developed for corresponding individual instruments or a master calibration model was developed on a particular master instrument, transferred to one or more other instruments, and adjusted with instrument-specific correction factors to standardize the predicted results across multiple instruments.
In determining the chemical properties of incoming raw materials such as tallow, coconut oil and palm kernel oil for the production of carboxylic acids, near-infrared spectrometry with appropriate chemometric techniques such as the partial least squares (PLS) method has been used to evaluate the free carboxylic acid content of the starting materials, as well as iodine value and moisture content. The near-infrared monitoring can also be used to monitor the progress of the transesterification process utilizing fatty triglycerides and methanol as reactants. A near-infrared spectrometer connected to transesterification process equipment can also monitor free glycerine content, bound/combined glycerine content and methyl ester concentration. Alternatively, samples can be taken during the progress of the reaction to a stand-alone near-infrared spectrometer loaded with appropriate calibration models for off-line analysis. In connection with the monitoring of the progress of a reaction, the near-infrared spectrometer can utilize a fiber optic probe connected to the spectrometer by fiber optic cable. The use of the near-infrared spectrometer in combination with the application of modeling software permits analysis of particular chemical species during the progress of chemical processing, as well as at the conclusion of the chemical process. Spectrometers such as near-infrared operating in the in-line mode are capable of providing data substantially on a real time basis. Data generation in these instances occurs under tightly controlled test and environmental conditions and involves one or more probes connected to a single instrument connected to a single data processing unit.
There is presently a high interest in the analysis of agricultural products. Genetically modified materials are of particular interest. The grain and food distribution segments in agriculture have expressed significant need for analytical technology to meet market requirements to identify and quantitate genetically modified crops, especially corn and soybean, in world markets. This need has developed rapidly. U.S. farmers have increasingly accepted crops derived from genetic engineering after the success they experienced in the 1996 growing season. The U.S. Department of Agriculture estimated that approximately 25% of U.S. corn and 54% of U.S. soybeans produced in 2000 were grown from genetically engineered seed with input traits to provide resistance to herbicides, insects, or both. The composition of such input trait crops is generally macroscopically indistinguishable from similar crops without the corresponding input traits.
In contrast, the foods of the future which will incorporate improvements of direct benefit to the consumer likely will be based at least in part on crops having enhanced output traits. The composition of these enhanced crops is different from the corresponding conventional crops. Examples include high oil corn, high sucrose soybeans, and low linolenic canola. Genetically-enhanced crops can be produced either by genetic engineering, as enabled by recent advances in biotechnology, or by specially designed traditional breeding programs. Even traditional crop improvement practices can result in plants with changed genetics and enhanced properties.
The growth and the need for analytical technology for agricultural products has been the promulgation of labeling regulations adopted in many regions of the world including the two largest agricultural commodity trading communities, the European Union and Japan. These labeling requirements have required or are expected to require food processors to label finished food products as to the genetically modified content of the ingredients used to produce these products. The initiation of labeling and the growing number of food processors electing to use raw materials which have not been genetically modified are driving the need for identity preservation.
Labeling specifications are nearing completion in both Europe and Japan. Identifying the genetic composition of grain in commercial crops and maintaining that identity throughout the agricultural complex to support labeling has become a high priority for seed companies, commercial growers, distribution and process companies, as well as food processors and is expected to increase as labeling is further implemented in the future. Consequently there is a need to provide an economical and efficient way to analyze seeds and crops at various locations along the supply chain, to identify and quantify the chemical composition and potentially other measurable properties of one or more output traits in genetically enhanced as well as conventional crops.
The interest in obtaining detailed analysis of agricultural products extends also into areas involving analysis of other materials. There remains a need as to other materials in providing an economical and efficient way of analyzing materials on site at remote locations to identify and quantify their chemical compositions and other properties of interest.